The present disclosure relates to image processing.
Despite the widespread availability of color cameras and color displays, black-and-white imagery is still used in artistic endeavors and in the design of algorithms in image processing and computer vision that require a single value per pixel. Black-and-white printing is also less expensive than color printing. Therefore, the conversion of color images to grayscale images is an interesting problem in many workflows. The goal is to preserve the visual information present in the original image while removing chrominance information.
Typical techniques for preserving visual information while mapping colors to grayscale attempt to preserve the difference between every pair of colors in the color image in the grayscale version of the image. After all differences have been computed, nonlinear minimization is typically done to find the grayscale value for each color that best preserves the color differences.
But such techniques can take a great deal of time to convert large color images and fail to provide an interactive experience to an end user. This is very important because users may not have a clear idea of what the desired output should look like, and many possibilities may need to be examined. Moreover, any color space uses a specific layout of colors that may not be desirable to users. For example, since red and green are opposites in the CIE-Lab color space, only one is commonly favored as being brighter in the grayscale image. Similarly, blue and yellow objects of equal luminance are commonly not both bright. An additional drawback is that such techniques may not allow users to control parameters for processes implementing such techniques.